Knowledge Discovery in Databases
Papers from the AAAI Workshop
Usama M. Fayyad and Ramasamy Uthurusamy, Cochairs
Knowledge discovery in databases (KDD) is an area of common interest for researchers in machine learning, machine discovery, statistics, intelligent databases, knowledge acquisition, data visualization and expert systems. The rapid growth of data and information created a need and an opportunity for extracting knowledge from databases, and both researchers and application developers have been responding to that need. KDD applications have been developed for astronomy, biology, finance, insurance, marketing, medicine, and many other fields. Core problems in KDD include representation issues, search complexity, the use of prior knowledge, and statistical inference. This one-and-a-half-day workshop brought together researchers and application developers from different areas, and focused on unifying themes such as the use of domain knowledge, managing uncertainty, interactive (human-oriented) presentation, and applications.